How deeply do you feel the growing problem of inequality in our society? The term 'wealth concentration' has become familiar, and solutions to address it are at the center of political discourse. However, how many people realize that the first step in examining inequality is 'numbers'? Beyond simple income statistics, the very method of interpreting and utilizing this data can serve as a yardstick for how accurately we perceive the problem of inequality. A recent analytical report published by the United Nations (UN), titled 'How unequal is the world? It depends on the data,' sharply dissects the complexities of measuring global inequality. Rahul Lahoti, an economist at the United Nations University World Institute for Development Economics Research (UNU-WIDER) who authored the report, points out that the reality depicted by data can dramatically change depending on the criteria used to measure inequality. He explains that India is a prime example. According to the World Bank's 'Poverty and Inequality Platform,' India's inequality index records relatively low figures. In contrast, UNU-WIDER and the World Inequality Lab present significantly higher inequality indices for the same country. Such conflicting assessments for a single country are not due to simple statistical errors. The core reason lies in what is being measured. Depending on whether income or consumption is used as the basis, whether tax records are included, and which data sources are utilized, the level of inequality appears entirely different. Lahoti emphasizes in the report that 'measuring inequality depends on a series of decisions, and each decision significantly impacts the final outcome.' He particularly highlights the substantial difference between consumption-based data, primarily used by the World Bank, and income and tax record-based data, prioritized by UNU-WIDER and the World Inequality Lab. This is where the difficulty in discussing inequality begins. For instance, many developing countries worldwide primarily use consumption data rather than income data. While this is partly due to practical limitations in accurately collecting income information, it also contains a significant trap. High-income individuals tend to save or invest a significant portion of their income, making inequality appear less severe when measured by consumption. Conversely, low-income individuals are often forced to spend most of their income on consumption. Consequently, consumption data tends to underestimate the actual proportion of wealth held by high-income groups. This issue becomes clearer with the concept of the 'missing rich.' Household income surveys often structurally fail to adequately capture the top 1% of high-income earners. This is because ultra-high-net-worth individuals tend not to respond to surveys or underreport their actual income. Lahoti emphasizes the absolute necessity of diverse data utilization, stating that 'when household survey data and tax records are combined, inequality estimates increase significantly.' Indeed, analysis by the World Inequality Lab using tax records showed that in many countries, the income share of the top 1% was more than double the estimates based on household surveys. In South Korea, the issue of inequality has also been continuously debated. Particularly, the deepening of inequality since the COVID-19 pandemic is threatening us in new ways. According to the Household Financial Welfare Survey published by Statistics Korea, the income quintile ratio (the income ratio between the top 20% and bottom 20%) based on market income slightly improved from 6.35 in 2020 to 6.18 in 2024. However, based on disposable income, it recorded 5.29 in 2024, indicating a significant role played by public transfer income, i.e., government subsidies and welfare expenditures. In other words, the fundamental income gap structure before government intervention has not yet been resolved. Hidden Blind Spots in Inequality Data What is more noteworthy is that in South Korea, too, the level of inequality varies depending on the measurement method. While Statistics Korea's Household Income and Expenditure Survey primarily focuses on consumption expenditures, the Household Financial Welfare Survey comprehensively covers income and assets. According to an analysis by the Korea Institute of Public Finance using National Tax Service tax data, the income share of the top 10% was significantly higher than estimates based on household surveys. This indicates that the 'missing rich' problem, pointed out by Lahoti, also exists in South Korea. In this situation, South Korea needs to be even more cautious in how it handles inequality data. The complexity of inequality data directly adds to the difficulties in policy-making. For example, suppose the government is considering new welfare policies to reduce inequality. If policy targeting is based on consumption data, there is a high probability of missing the truly impoverished population captured
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